Overview

Dataset statistics

Number of variables11
Number of observations6823
Missing cells9920
Missing cells (%)13.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory586.5 KiB
Average record size in memory88.0 B

Variable types

NUM8
CAT2
BOOL1

Warnings

Forty has 170 (2.5%) missing values Missing
Vertical has 1563 (22.9%) missing values Missing
BenchReps has 2194 (32.2%) missing values Missing
BroadJump has 1617 (23.7%) missing values Missing
Cone has 2223 (32.6%) missing values Missing
Shuttle has 2153 (31.6%) missing values Missing
Player has unique values Unique

Reproduction

Analysis started2021-01-05 11:05:39.791500
Analysis finished2021-01-05 11:05:49.534069
Duration9.74 seconds
Software versionpandas-profiling v2.9.0
Download configurationconfig.yaml

Variables

Player
Categorical

UNIQUE

Distinct6823
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size53.3 KiB
Tim Day
 
1
LaMarr Woodley
 
1
Kedric Golston
 
1
Johnnie Morant
 
1
Chris Lindstrom
 
1
Other values (6818)
6818 
ValueCountFrequency (%) 
Tim Day1< 0.1%
 
LaMarr Woodley1< 0.1%
 
Kedric Golston1< 0.1%
 
Johnnie Morant1< 0.1%
 
Chris Lindstrom1< 0.1%
 
LaDarius Perkins1< 0.1%
 
Curry Burns1< 0.1%
 
DeAndre Jackson1< 0.1%
 
Adam Haayer1< 0.1%
 
Lance Nimmo1< 0.1%
 
Other values (6813)681399.9%
 
2021-01-05T19:05:49.623965image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique6823 ?
Unique (%)100.0%
2021-01-05T19:05:49.761672image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length27
Median length13
Mean length12.8903708
Min length7

Pos
Categorical

Distinct27
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size53.3 KiB
WR
942 
CB
694 
RB
597 
DE
500 
DT
480 
Other values (22)
3610 
ValueCountFrequency (%) 
WR94213.8%
 
CB69410.2%
 
RB5978.7%
 
DE5007.3%
 
DT4807.0%
 
OT4676.8%
 
OLB4246.2%
 
QB3835.6%
 
TE3785.5%
 
OG3775.5%
 
Other values (17)158123.2%
 
2021-01-05T19:05:49.892114image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2021-01-05T19:05:50.003806image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length4
Median length2
Mean length2.040891104
Min length1

Ht
Real number (ℝ≥0)

Distinct18
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean73.81430456
Minimum65
Maximum82
Zeros0
Zeros (%)0.0%
Memory size53.3 KiB
2021-01-05T19:05:50.103587image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum65
5-th percentile69
Q172
median74
Q376
95-th percentile78
Maximum82
Range17
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.616382881
Coefficient of variation (CV)0.03544547221
Kurtosis-0.4588760342
Mean73.81430456
Median Absolute Deviation (MAD)2
Skewness-0.1540872474
Sum503635
Variance6.845459378
MonotocityNot monotonic
2021-01-05T19:05:50.197332image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%) 
7597214.2%
 
7489713.1%
 
7685912.6%
 
7383912.3%
 
7277611.4%
 
776199.1%
 
716038.8%
 
704306.3%
 
782984.4%
 
692393.5%
 
Other values (8)2914.3%
 
ValueCountFrequency (%) 
651< 0.1%
 
66100.1%
 
67330.5%
 
68781.1%
 
692393.5%
 
ValueCountFrequency (%) 
822< 0.1%
 
8140.1%
 
80380.6%
 
791251.8%
 
782984.4%
 

Wt
Real number (ℝ≥0)

Distinct206
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean242.9860765
Minimum149
Maximum375
Zeros0
Zeros (%)0.0%
Memory size53.3 KiB
2021-01-05T19:05:50.321003image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum149
5-th percentile186
Q1206
median233
Q3279
95-th percentile320
Maximum375
Range226
Interquartile range (IQR)73

Descriptive statistics

Standard deviation44.96686894
Coefficient of variation (CV)0.185059447
Kurtosis-0.9099421689
Mean242.9860765
Median Absolute Deviation (MAD)31
Skewness0.5227053648
Sum1657894
Variance2022.019302
MonotocityNot monotonic
2021-01-05T19:05:50.448628image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
211931.4%
 
205871.3%
 
210871.3%
 
204861.3%
 
195831.2%
 
213831.2%
 
209811.2%
 
193811.2%
 
197801.2%
 
202791.2%
 
Other values (196)598387.7%
 
ValueCountFrequency (%) 
1491< 0.1%
 
1551< 0.1%
 
1561< 0.1%
 
1602< 0.1%
 
1631< 0.1%
 
ValueCountFrequency (%) 
3751< 0.1%
 
3701< 0.1%
 
3692< 0.1%
 
3661< 0.1%
 
3641< 0.1%
 

Forty
Real number (ℝ≥0)

MISSING

Distinct160
Distinct (%)2.4%
Missing170
Missing (%)2.5%
Infinite0
Infinite (%)0.0%
Mean4.880455434
Minimum4.22
Maximum9.99
Zeros0
Zeros (%)0.0%
Memory size53.3 KiB
2021-01-05T19:05:50.576890image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum4.22
5-th percentile4.41
Q14.54
median4.71
Q35
95-th percentile5.42
Maximum9.99
Range5.77
Interquartile range (IQR)0.46

Descriptive statistics

Standard deviation0.7787962248
Coefficient of variation (CV)0.1595744978
Kurtosis33.01849254
Mean4.880455434
Median Absolute Deviation (MAD)0.2
Skewness5.463036234
Sum32469.67
Variance0.6065235598
MonotocityNot monotonic
2021-01-05T19:05:50.695543image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
4.51492.2%
 
4.561362.0%
 
4.651352.0%
 
4.621321.9%
 
9.991291.9%
 
4.581241.8%
 
4.521221.8%
 
4.591221.8%
 
4.531211.8%
 
4.61121.6%
 
Other values (150)537178.7%
 
(Missing)1702.5%
 
ValueCountFrequency (%) 
4.222< 0.1%
 
4.241< 0.1%
 
4.261< 0.1%
 
4.273< 0.1%
 
4.2850.1%
 
ValueCountFrequency (%) 
9.991291.9%
 
6.051< 0.1%
 
61< 0.1%
 
5.991< 0.1%
 
5.861< 0.1%
 

Vertical
Real number (ℝ≥0)

MISSING

Distinct56
Distinct (%)1.1%
Missing1563
Missing (%)22.9%
Infinite0
Infinite (%)0.0%
Mean32.88697719
Minimum17.5
Maximum46
Zeros0
Zeros (%)0.0%
Memory size53.3 KiB
2021-01-05T19:05:50.847363image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum17.5
5-th percentile25.5
Q130
median33
Q336
95-th percentile39.5
Maximum46
Range28.5
Interquartile range (IQR)6

Descriptive statistics

Standard deviation4.200753695
Coefficient of variation (CV)0.1277330437
Kurtosis-0.1676728427
Mean32.88697719
Median Absolute Deviation (MAD)3
Skewness-0.2013368553
Sum172985.5
Variance17.64633161
MonotocityNot monotonic
2021-01-05T19:05:50.967078image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
332754.0%
 
342593.8%
 
33.52563.8%
 
35.52473.6%
 
352393.5%
 
34.52373.5%
 
362333.4%
 
32.52333.4%
 
322203.2%
 
311942.8%
 
Other values (46)286742.0%
 
(Missing)156322.9%
 
ValueCountFrequency (%) 
17.51< 0.1%
 
191< 0.1%
 
19.53< 0.1%
 
201< 0.1%
 
20.570.1%
 
ValueCountFrequency (%) 
461< 0.1%
 
45.51< 0.1%
 
4540.1%
 
44.52< 0.1%
 
443< 0.1%
 

BenchReps
Real number (ℝ≥0)

MISSING

Distinct45
Distinct (%)1.0%
Missing2194
Missing (%)32.2%
Infinite0
Infinite (%)0.0%
Mean20.83063297
Minimum2
Maximum49
Zeros0
Zeros (%)0.0%
Memory size53.3 KiB
2021-01-05T19:05:51.106672image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile11
Q116
median21
Q325
95-th percentile32
Maximum49
Range47
Interquartile range (IQR)9

Descriptive statistics

Standard deviation6.362080028
Coefficient of variation (CV)0.3054194291
Kurtosis0.08944848128
Mean20.83063297
Median Absolute Deviation (MAD)4
Skewness0.2612411521
Sum96425
Variance40.47606228
MonotocityNot monotonic
2021-01-05T19:05:51.237323image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%) 
192894.2%
 
212824.1%
 
232784.1%
 
202663.9%
 
242653.9%
 
222653.9%
 
172563.8%
 
182533.7%
 
152433.6%
 
252213.2%
 
Other values (35)201129.5%
 
(Missing)219432.2%
 
ValueCountFrequency (%) 
21< 0.1%
 
32< 0.1%
 
450.1%
 
550.1%
 
6100.1%
 
ValueCountFrequency (%) 
491< 0.1%
 
453< 0.1%
 
4440.1%
 
431< 0.1%
 
4240.1%
 

BroadJump
Real number (ℝ≥0)

MISSING

Distinct62
Distinct (%)1.2%
Missing1617
Missing (%)23.7%
Infinite0
Infinite (%)0.0%
Mean114.3301959
Minimum74
Maximum147
Zeros0
Zeros (%)0.0%
Memory size53.3 KiB
2021-01-05T19:05:51.397304image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum74
5-th percentile97
Q1109
median115
Q3121
95-th percentile128
Maximum147
Range73
Interquartile range (IQR)12

Descriptive statistics

Standard deviation9.320294789
Coefficient of variation (CV)0.08152085031
Kurtosis-0.0002723226255
Mean114.3301959
Median Absolute Deviation (MAD)6
Skewness-0.4149142964
Sum595203
Variance86.86789495
MonotocityNot monotonic
2021-01-05T19:05:51.522492image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
1202774.1%
 
1182523.7%
 
1172353.4%
 
1162343.4%
 
1152343.4%
 
1212323.4%
 
1192103.1%
 
1132103.1%
 
1141972.9%
 
1121912.8%
 
Other values (52)293443.0%
 
(Missing)161723.7%
 
ValueCountFrequency (%) 
741< 0.1%
 
781< 0.1%
 
822< 0.1%
 
841< 0.1%
 
8540.1%
 
ValueCountFrequency (%) 
1471< 0.1%
 
1413< 0.1%
 
1401< 0.1%
 
1393< 0.1%
 
1383< 0.1%
 

Cone
Real number (ℝ≥0)

MISSING

Distinct294
Distinct (%)6.4%
Missing2223
Missing (%)32.6%
Infinite0
Infinite (%)0.0%
Mean7.349841304
Minimum3.97
Maximum9.99
Zeros0
Zeros (%)0.0%
Memory size53.3 KiB
2021-01-05T19:05:51.916516image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum3.97
5-th percentile6.63
Q16.96
median7.2
Q37.62
95-th percentile9.99
Maximum9.99
Range6.02
Interquartile range (IQR)0.66

Descriptive statistics

Standard deviation0.9601758808
Coefficient of variation (CV)0.1306389949
Kurtosis4.146354028
Mean7.349841304
Median Absolute Deviation (MAD)0.3
Skewness0.2381421691
Sum33809.27
Variance0.9219377221
MonotocityNot monotonic
2021-01-05T19:05:52.030553image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
9.992894.2%
 
7.09761.1%
 
7.07751.1%
 
7671.0%
 
7.08661.0%
 
6.9630.9%
 
7.12590.9%
 
7.15580.9%
 
6.94540.8%
 
6.96540.8%
 
Other values (284)373954.8%
 
(Missing)222332.6%
 
ValueCountFrequency (%) 
3.971< 0.1%
 
3.991< 0.1%
 
4.011< 0.1%
 
4.032< 0.1%
 
4.041< 0.1%
 
ValueCountFrequency (%) 
9.992894.2%
 
9.121< 0.1%
 
9.041< 0.1%
 
91< 0.1%
 
8.841< 0.1%
 

Shuttle
Real number (ℝ≥0)

MISSING

Distinct236
Distinct (%)5.1%
Missing2153
Missing (%)31.6%
Infinite0
Infinite (%)0.0%
Mean4.836351178
Minimum3.73
Maximum9.99
Zeros0
Zeros (%)0.0%
Memory size53.3 KiB
2021-01-05T19:05:52.158324image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum3.73
5-th percentile4.04
Q14.21
median4.39
Q34.67
95-th percentile9.99
Maximum9.99
Range6.26
Interquartile range (IQR)0.46

Descriptive statistics

Standard deviation1.445666638
Coefficient of variation (CV)0.2989168043
Kurtosis7.355668142
Mean4.836351178
Median Absolute Deviation (MAD)0.21
Skewness2.927457052
Sum22585.76
Variance2.089952029
MonotocityNot monotonic
2021-01-05T19:05:52.288940image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
9.992904.3%
 
4.41051.5%
 
4.281011.5%
 
4.21941.4%
 
4.2921.3%
 
4.15841.2%
 
4.25841.2%
 
4.18821.2%
 
4.32811.2%
 
4.07771.1%
 
Other values (226)358052.5%
 
(Missing)215331.6%
 
ValueCountFrequency (%) 
3.731< 0.1%
 
3.751< 0.1%
 
3.781< 0.1%
 
3.81< 0.1%
 
3.812< 0.1%
 
ValueCountFrequency (%) 
9.992904.3%
 
8.281< 0.1%
 
8.151< 0.1%
 
8.131< 0.1%
 
8.061< 0.1%
 

pro bowl?
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size53.3 KiB
0
6248 
1
 
575
ValueCountFrequency (%) 
0624891.6%
 
15758.4%
 
2021-01-05T19:05:52.392694image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Interactions

2021-01-05T19:05:40.446118image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-05T19:05:40.587739image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-05T19:05:40.728399image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-05T19:05:40.855054image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-05T19:05:40.980712image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-05T19:05:41.093400image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-05T19:05:41.223053image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-05T19:05:41.349715image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-05T19:05:41.473397image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-05T19:05:41.614023image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-05T19:05:41.765616image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-05T19:05:41.889285image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-05T19:05:42.017941image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-05T19:05:42.144603image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-05T19:05:42.281236image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-05T19:05:42.407898image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-05T19:05:42.533562image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-05T19:05:42.661220image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-05T19:05:42.790873image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-05T19:05:42.911864image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-05T19:05:43.033226image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-05T19:05:43.149913image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-05T19:05:43.273587image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-05T19:05:43.398763image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-05T19:05:43.516449image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-05T19:05:43.632139image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-05T19:05:43.751819image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-05T19:05:43.874491image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-05T19:05:43.992177image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-05T19:05:44.104875image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-05T19:05:44.227547image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-05T19:05:44.345233image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-05T19:05:44.459925image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-05T19:05:44.571626image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-05T19:05:45.091797image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-05T19:05:45.198512image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-05T19:05:45.309216image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-05T19:05:45.422911image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-05T19:05:45.544590image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-05T19:05:45.650815image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-05T19:05:45.766506image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-05T19:05:45.897157image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-05T19:05:46.034788image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-05T19:05:46.161450image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-05T19:05:46.285119image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-05T19:05:46.406793image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-05T19:05:46.521486image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-05T19:05:46.643161image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-05T19:05:46.766830image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-05T19:05:46.884515image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-05T19:05:47.017160image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-05T19:05:47.141827image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-05T19:05:47.267491image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-05T19:05:47.386208image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-05T19:05:47.504377image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-05T19:05:47.622063image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-05T19:05:47.747301image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-05T19:05:47.876955image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-05T19:05:48.014587image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-05T19:05:48.134266image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-05T19:05:48.246199image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-05T19:05:48.363890image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-05T19:05:48.506539image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-05T19:05:48.632726image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Correlations

2021-01-05T19:05:52.470487image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-01-05T19:05:52.651047image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-01-05T19:05:52.822601image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-01-05T19:05:53.000708image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2021-01-05T19:05:48.851491image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-05T19:05:49.089003image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-05T19:05:49.271090image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-05T19:05:49.426642image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Sample

First rows

PlayerPosHtWtFortyVerticalBenchRepsBroadJumpConeShuttlepro bowl?
0John AbrahamOLB762524.55NaNNaNNaNNaNNaN1
1Shaun AlexanderRB722184.58NaNNaNNaNNaNNaN1
2Darnell AlfordOT763345.5625.023.094.08.484.980
3Kyle AllamonTE742534.9729.0NaN104.07.294.490
4Rashard AndersonCB742064.5534.0NaN123.07.184.150
5Jake AriansK70202NaNNaNNaNNaNNaNNaN0
6LaVar ArringtonOLB752504.53NaNNaNNaNNaNNaN1
7Corey AtkinsOLB722374.7231.021.0112.07.964.390
8Kyle AtteberryK72167NaNNaNNaNNaNNaNNaN0
9Reggie AustinCB691754.4435.017.0119.07.034.140

Last rows

PlayerPosHtWtFortyVerticalBenchRepsBroadJumpConeShuttlepro bowl?
6813Rob WindsorDL762854.9028.521.0111.07.474.440
6814Antoine WinfieldS702054.4536.0NaN124.09.999.991
6815Tristan WirfsOL773224.8536.524.0121.07.654.680
6816Steven WirtelLS762274.7626.0NaN120.07.124.280
6817Charlie WoernerTE772454.7834.521.0120.07.184.460
6818D.J. WonnumDL772544.7334.520.0123.07.254.440
6819Dom Wood-AndersonTE762574.9235.0NaN119.09.999.990
6820David WoodwardLB742354.7933.516.0114.07.344.370
6821Chase YoungDL772659.99NaNNaNNaN9.999.991
6822Jabari ZunigaDL752534.6433.029.0127.09.999.990